Abstract: A device, system, and method for assessing and managing resilience of an individual, particularly in regard to levels of stress, anxiety, and hardship the individual is capable of handling. A system is configured to evaluate physiological data of the individual and to estimate a resilience of the individual based on the evaluation. The resilience may relate to an ability of the individual to manage, or not manage, a stressor based on one or more resilience parameters. The system may intervene to help the individual improve their resilience, for example, if the individual has not coped with and/or recovered from a past stressor, is not coping with and/or recovering from a current stressor, and/or is not prepared to cope with and/or recover from a future stressor.

Abstract: A device, system, and method for assessing and managing mental health of an individual. A device is configured to evaluate data which corresponds to one or more biosignals of the individual, and to detect the intensity of both positive and negative emotional valence in the individual. The individual may have neither positive nor negative emotional valence (e.g., when at rest), only one of positive and negative emotional valence, or some amount of both. If the detected negative emotional valance surpasses a first threshold, the device or system may provide an intervention to the individual to help the individual manage stress and improve mental health. If the detected positive emotional valence surpasses a second threshold, the device or system may record information related to the individual's stress level and provide encouragement to help the individual continue the positive activity.

Abstract: A system for health monitoring comprised of one or more neural networks. A first function of the one or more neural networks includes an autoencoder functionality, tuned to have high recall, configured for monitoring data and for detecting an anomaly within the data. A second function of the one or more neural networks is a false positive reduction (FPR) function, configured for distinguishing false positive anomalies from true positive anomalies by analyzing the data or querying an individual. Confirmed positive anomalies are classified by a classification engine. The system may be configured to train one or more machine learning models thereof, such that the system is trained for monitoring of the individual.

Abstract: A system for management of health conditions, such as mental health conditions, using a biosensor configured to monitor one or more biological properties of an individual and providing feedback and information based on data in real-time. A corresponding method, performed by one or more components of the system, may include monitoring abiological property of the individual, identifying an irregularity as a symptom of the health condition, and intervening with the individual to alleviate the symptom. The method may further include determining an efficacy of the intervention and adapting the method to improve the intervention. The system may be configured to predict a future symptom and preventatively intervene.

Abstract: A smart home interaction system is presented. It is built on a multi-modal, multithreaded conversational dialog engine. The system provides a natural language user interface for the control of household devices, appliances or household functionality. The smart home automation agent can receive input from users through sensing devices such as a smart phone, a tablet computer or a laptop computer. Users interact with the system from within the household or from remote locations. The smart home system can receive input from sensors or any other machines with which it is interfaced. The system employs interaction guide rules for processing reaction to both user and sensor input and driving the conversational interactions that result from such input. The system adaptively learns based on both user and sensor input and can learn the preferences and practices of its users.

Abstract: A Mechanical Turk—Integrated Development Environment system is disclosed. An integrated development environment (IDE) can include one or more interfaces capable of communicating with a mechanical turk engine. As a developer creates applications within the IDE, the developer can use the IDE to submit one or more requests to the mechanical turk engine. The engine constructs a mechanical turk project based on the requests and provides project tasks to workers. The results of the tasks can then be compiled and integrated back into the developer's application via the IDE. An example use includes constructing large domain specific data sets that can be applied to spoken dialog interfaces.

Abstract: A device capable of splitting user input into phrases is presented. The disclosed device leverages multiple phrase splitting models to generate one or more possible split locations. The possible split locations can be derived based on leveraging multiple phrase splitting models. Each model contributes its suggested split locations to the set of possible split locations according to an implementation of a phrase splitting kernel algorithm that weights each model's suggestions.

Abstract: The invention enables creation of grammar networks that can regulate, control, and define the content and scope of human-machine interaction in natural language voice user interfaces (NLVUI). More specifically, the invention concerns a phrase-based modeling of generic structures of verbal interaction and use of these models for the purpose of automating part of the design of such grammar networks.

Abstract: A device capable of splitting user input into phrases is presented. The disclosed device leverages multiple phrase splitting models to generate one or more possible split locations. The possible split locations can be derived based on leveraging multiple phrase splitting models. Each model contributes its suggested split locations to the set of possible split locations according to an implementation of a phrase splitting kernel algorithm that weights each model's suggestions.

Abstract: A smart home interaction system is presented. It is built on a multi-modal, multithreaded conversational dialog engine. The system provides a natural language user interface for the control of household devices, appliances or household functionality. The smart home automation agent can receive input from users through sensing devices such as a smart phone, a tablet computer or a laptop computer. Users interact with the system from within the household or from remote locations. The smart home system can receive input from sensors or any other machines with which it is interfaced. The system employs interaction guide rules for processing reaction to both user and sensor input and driving the conversational interactions that result from such input. The system adaptively learns based on both user and sensor input and can learn the preferences and practices of its users.

Abstract: A smart home interaction system is presented. It is built on a multi-modal, multithreaded conversational dialog engine. The system provides a natural language user interface for the control of household devices, appliances or household functionality. The smart home automation agent can receive input from users through sensing devices such as a smart phone, a tablet computer or a laptop computer. Users interact with the system from within the household or from remote locations. The smart home system can receive input from sensors or any other machines with which it is interfaced. The system employs interaction guide rules for processing reaction to both user and sensor input and driving the conversational interactions that result from such input. The system adaptively learns based on both user and sensor input and can learn the preferences and practices of its users.

Abstract: A device capable of splitting user input into phrases is presented. The disclosed device leverages multiple phrase splitting models to generate one or more possible split locations. The possible split locations can be derived based on leveraging multiple phrase splitting models. Each model contributes its suggested split locations to the set of possible split locations according to an implementation of a phrase splitting kernel algorithm that weights each model's suggestions.

Abstract: A Mechanical Turk—Integrated Development Environment system is disclosed. An integrated development environment (IDE) can include one or more interfaces capable of communicating with a mechanical turk engine. As a developer creates applications within the IDE, the developer can use the IDE to submit one or more requests to the mechanical turk engine. The engine constructs a mechanical turk project based on the requests and provides project tasks to workers. The results of the tasks can then be compiled and integrated back into the developer's application via the IDE. An example use includes constructing large domain specific data sets that can be applied to spoken dialog interfaces.

Abstract: A Mechanical Turk-Integrated Development Environment system is disclosed. An integrated development environment (IDE) can include one or more interfaces capable of communicating with a mechanical turk engine. As a developer creates applications within the IDE, the developer can use the IDE to submit one or more requests to the mechanical turk engine. The engine constructs a mechanical turk project based on the requests and provides project tasks to workers. The results of the tasks can then be compiled and integrated back into the developer's application via the IDE. An example use includes constructing large domain specific data sets that can be applied to spoken dialog interfaces.

Abstract: A smart home interaction system is presented. It is built on a multi-modal, multithreaded conversational dialog engine. The system provides a natural language user interface for the control of household devices, appliances or household functionality. The smart home automation agent can receive input from users through sensing devices such as a smart phone, a tablet computer or a laptop computer. Users interact with the system from within the household or from remote locations. The smart home system can receive input from sensors or any other machines with which it is interfaced. The system employs interaction guide rules for processing reaction to both user and sensor input and driving the conversational interactions that result from such input. The system adaptively learns based on both user and sensor input and can learn the preferences and practices of its users.

Abstract: A smart home interaction system is presented. It is built on a multi-modal, multithreaded conversational dialog engine. The system provides a natural language user interface for the control of household devices, appliances or household functionality. The smart home automation agent can receive input from users through sensing devices such as a smart phone, a tablet computer or a laptop computer. Users interact with the system from within the household or from remote locations. The smart home system can receive input from sensors or any other machines with which it is interfaced. The system employs interaction guide rules for processing reaction to both user and sensor input and driving the conversational interactions that result from such input. The system adaptively learns based on both user and sensor input and can learn the preferences and practices of its users.

Abstract: A Mechanical Turk-Integrated Development Environment system is disclosed. An integrated development environment (IDE) can include one or more interfaces capable of communicating with a mechanical turk engine. As a developer creates applications within the IDE, the developer can use the IDE to submit one or more requests to the mechanical turk engine. The engine constructs a mechanical turk project based on the requests and provides project tasks to workers. The results of the tasks can then be compiled and integrated back into the developer's application via the IDE. An example use includes constructing large domain specific data sets that can be applied to spoken dialog interfaces.

Abstract: A Mechanical Turk-Integrated Development Environment system is disclosed. An integrated development environment (IDE) can include one or more interfaces capable of communicating with a mechanical turk engine. As a developer creates applications within the IDE, the developer can use the IDE to submit one or more requests to the mechanical turk engine. The engine constructs a mechanical turk project based on the requests and provides project tasks to workers. The results of the tasks can then be compiled and integrated back into the developer's application via the IDE. An example use includes constructing large domain specific data sets that can be applied to spoken dialog interfaces.

Abstract: The performance of traditional speech recognition systems (as applied to information extraction or translation) decreases significantly with, larger domain size, scarce training data as well as under noisy environmental conditions. This invention mitigates these problems through the introduction of a novel predictive feature extraction method which combines linguistic and statistical information for representation of information embedded in a noisy source language. The predictive features are combined with text classifiers to map the noisy text to one of the semantically or functionally similar groups. The features used by the classifier can be syntactic, semantic, and statistical.

Abstract: A device capable of splitting user input into phrases is presented. The disclosed device leverages multiple phrase splitting models to generate one or more possible split locations. The possible split locations can be derived based on leveraging multiple phrase splitting models. Each model contributes its suggested split locations to the set of possible split locations according to an implementation of a phrase splitting kernel algorithm that weights each model's suggestions.